rRNA Sequence Data Research Articles

Removal of organic pollutants from paper mill effluent using Taro (Colocasia esculenta L. Schott) in an electro-assisted horizontal subsurface flow constructed wetland: Experimental and kinetic studies.

In this study, the phytoremediation potential of Taro (Colocasia esculenta L. Schott) plant was examined, utilizing horizontal subsurface flow constructed wetlands with and without an electric current supply for the purpose of removing pollutants from paper mill effluent. For this, different wetlands were set up with varying concentrations of effluent: CW (Control), CW1 (25%), CW2 (50%), CW3 (75%), CW4 (100%). After 45days, the highest plant height (85.13 ± 4.24cm), leaf area index( 250.83 ± 10.14), fresh biomass (565.30 ± 6 .10g), root biomass (392.85 ± 4.34g), root-to-shoot ratio (2.41 ± 2.10), relative growth rate (0.044 ± 0.002 gg-1d-1), and chlorophyll content (3.29 ± 0.07mg/g fwt) was observed in CW2 with current supply, along with significant removal of pollutants (pH: 7.13 ± 0.15, EC: 2.33 ± 0.07 dS/m, TDS: 192.52 ± 6.12mg/L, COD: 490.17 ± 5.01mg/L, BOD: 206.74 ± 5.92mg/L, potassium: 73.27 ± 4.11mg/L, sodium: 46.62 ± 2.27mg/L, phosphate phosphorus: 34.08 ± 1.43mg/L, and nitrate nitrogen: 104.85 ± 5.94mg/L) and highest first-order rate constant (k) values. Furthermore, the microbial community assessment of constructed wetlands using V3-V4 16S rRNA sequence data was prepared on the Illumina MiSeq framework. The major phyla identified were Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Chloroflexi, Acidobacteria, Nitrospirae, Planctomycetes, and others. The findings offer innovative insights for sustainable wastewater treatment strategies through phytoremediation of paper mill effluent using Taro plants in modified constructed wetlands and highlight the role of diverse microbial communities capable of degrading various pollutants in wastewater.

Constructing phylogenetic trees for microbiome data analysis: A mini-review

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes. In contrast, metagenomic and whole genome shotgun (WGS) sequencing involve sequencing from random fragments of the entire gene, making identification of consistent marker genes challenging owing to the vast diversity of genomic regions, resulting in a scarcity of robust tools for constructing phylogenetic trees. Although bacterial sequence tree construction tools exist for upstream bioinformatics, many downstream researchers—those integrating these trees into statistical models or machine learning—are either unaware of these tools or find them difficult to use due to the steep learning curve of processing raw sequences. This is compounded by the fact that public datasets often lack phylogenetic trees, providing only abundance tables and taxonomic classifications. To address this, we present a comprehensive review of phylogenetic tree construction techniques for microbiome data (16S rRNA or whole-genome shotgun sequencing). We outline the strengths and limitations of current methods, offering expert insights and step-by-step guidance to make these tools more accessible and widely applicable in quantitative microbiome data analysis.

Optimizing microbiome reference databases with PacBio full-length 16S rRNA sequencing for enhanced taxonomic classification and biomarker discovery.

The study of the human microbiome is crucial for understanding disease mechanisms, identifying biomarkers, and guiding preventive measures. Advances in sequencing platforms, particularly 16S rRNA sequencing, have revolutionized microbiome research. Despite the benefits, large microbiome reference databases (DBs) pose challenges, including computational demands and potential inaccuracies. This study aimed to determine if full-length 16S rRNA sequencing data produced by PacBio could be used to optimize reference DBs and be applied to Illumina V3-V4 targeted sequencing data for microbial study. Oral and gut microbiome data (PRJNA1049979) were retrieved from NCBI. DADA2 was applied to full-length 16S rRNA PacBio data to obtain amplicon sequencing variants (ASVs). The RDP reference DB was used to assign the ASVs, which were then used as a reference DB to train the classifier. QIIME2 was used for V3-V4 targeted Illumina data analysis. BLAST was used to analyze alignment statistics. Linear discriminant analysis Effect Size (LEfSe) was employed for discriminant analysis. ASVs produced by PacBio showed coverage of the oral microbiome similar to the Human Oral Microbiome Database. A phylogenetic tree was trimmed at various thresholds to obtain an optimized reference DB. This established method was then applied to gut microbiome data, and the optimized gut microbiome reference DB provided improved taxa classification and biomarker discovery efficiency. Full-length 16S rRNA sequencing data produced by PacBio can be used to construct a microbiome reference DB. Utilizing an optimized reference DB can increase the accuracy of microbiome classification and enhance biomarker discovery.

Synthetic communities derived from the core endophytic microbiome of hyperaccumulators and their role in cadmium phytoremediation

BackgroundAlthough numerous endophytic bacteria have been isolated and characterized from cadmium (Cd) hyperaccumulators, the contribution and potential application of the core endophytic microbiomes on facilitating phytoremediation were still lack of intensive recognition. Therefore, a 2-year field sampling in different location were firstly conducted to identify the unique core microbiome in Cd hyperaccumulators, among which the representative cultivable bacteria of different genera were then selected to construct synthetic communities (SynComs). Finally, the effects and mechanisms of the optimized SynCom in regulating Cd accumulation in different ecotypes of Sedum alfredii were studied to declare the potential application of the bacterial agents based on core microbiome.ResultsThrough an innovative network analysis workflow, 97 core bacterial taxa unique to hyperaccumulator Sedum was identified based on a 2-year field 16S rRNA sequencing data. A SynCom comprising 13 selected strains belonging to 6 different genera was then constructed. Under the combined selection pressure of the plant and Cd contamination, Alcaligenes sp. exhibited antagonistic relationships with other genera and plant Cd concentration. Five representative strains of the other five genera were further conducted genome resequencing and developed six SynComs, whose effects on Cd phytoremediation were compared with single strains by hydroponic experiments. The results showed that SynCom-NS comprising four strains (including Leifsonia shinshuensis, Novosphingobium lindaniclasticum, Ochrobactrum anthropi, and Pseudomonas izuensis) had the greatest potential to enhance Cd phytoremediation. After inoculation with SynCom-NS, genes related to Cd transport, antioxidative defense, and phytohormone signaling pathways were significantly upregulated in both ecotypes of S. alfredii, so as to promote plant growth, Cd uptake, and translocation.ConclusionIn this study, we designed an innovative network analysis workflow to identify the core endophytic microbiome in hyperaccumulator. Based on the cultivable core bacteria, an optimized SynCom-NS was constructed and verified to have great potential in enhancing phytoremediation. This work not only provided a framework for identifying core microbiomes associated with specific features but also paved the way for the construction of functional synthetic communities derived from core microbiomes to develop high efficient agricultural agents.5KavnCJpVp3k2g6uQCMsTeVideo

Differential impact of Paenibacillus infection on the microbiota of Varroa destructor and Apis mellifera

The Western honey bee (Apis mellifera) is a vital agricultural pollinator whose populations are threatened by the parasitic mite Varroa destructor and associated pathogens. While the impact of Paenibacillus species on honey bees, particularly Paenibacillus larvae causing American foulbrood, is documented, their effect on the microbiota of Varroa mites remains unclear. This study aimed to investigate the influence of Paenibacillus sp. on the bacterial communities of Varroa mites and adult honey bees. We hypothesized that Paenibacillus sp. would significantly alter the microbiota of Varroa mites but have minimal effect on that of adult honey bees. Utilizing 16S rRNA sequencing data from a previous study, we reanalyzed samples categorized into four groups based on Paenibacillus sp. infection load: highly infected and lowly infected honey bees (A. mellifera) and mites (V. destructor). Infection status was determined by Paenibacillus sp. read counts, with more than three reads indicating high infection. Microbial diversity was assessed using alpha and beta diversity metrics. Co-occurrence networks were constructed to visualize bacterial community assemblies, and network robustness was evaluated through node addition and removal tests. Keystone taxa were identified based on eigenvector centrality and relative abundance. Highly infected Varroa mites exhibited a significant reduction in alpha diversity and a markedly different bacterial community composition compared to lowly infected mites (p < 0.05). Their bacterial co-occurrence networks showed decreased connectivity and robustness, indicating a disruptive effect of Paenibacillus sp. In contrast, adult honey bees displayed no significant differences in alpha diversity or network structure between highly and lowly infected groups (p > 0.05), suggesting a resilient microbiota. Keystone taxa analysis revealed fewer central species in highly infected Varroa mites, potentially impacting network stability. High Paenibacillus sp. infection is associated with significant alterations in the microbiota of Varroa mites, disrupting bacterial communities and potentially affecting mite physiology. The microbiota of adult honey bees appears more robust against Paenibacillus sp. influence. These findings enhance our understanding of the complex interactions within the "honey bee–mite–microorganism" system and may inform future strategies for managing Varroa mite infestations and associated pathogens.

Integrative taxonomy of Tethya: description of four new species based on morphology, phylogeny and microbiome diversity

The genus Tethya, one of the most iconic groups in the phylum Porifera. includes nearly 100 valid species. Tethya shows a nearly cosmopolitan distribution, and thus this clade could help elucidate global mechanisms of speciation in sponges. Contrasting with many other marine organisms, Tethya peaks in diversity in the temperate region, with only ∼30% of diversity occurring in the tropics. This pattern may however be related to a lack of studies in the tropics, masked by dubious taxonomic identifications. To address this question, we studied new collections from the Western Atlantic (Caribbean and Brazil) and the Northeastern Atlantic, as well as museum material from the Indian Ocean. Combining morphological investigation with molecular phylogenetics and the study of the sponge’s microbial communities, we conclude that four constitute new species that we describe here: Tethya martini Riesgo, Giribet, & Santodomingo, sp. nov.; Tethya simoni Santodomingo, Zea, & Riesgo, sp. nov.; Tethya erici Díez-Vives, Santodomingo, Moles, & Riesgo, sp. nov.; Tethya orioni Kenny, Santodomingo & Riesgo, sp. nov. Some species thought to be widespread (e.g., T. aurantium and T. seychellensis), each represent multiple species with unique geographic distributions. A phylogenetic analysis of Tethya (based on COI and 28S rRNA sequence data) recovered five main clades, which were also characterized by distinct prokaryotic communities. This suggests that microbiomes could be used as a guide for taxonomic identification in Tethya. We finally explored the existence of a phylosymbiotic pattern in sponges at different levels of prokaryotic taxonomic resolution (i.e., at phylum, class, and genus-level analysis of prokaryotes). Remarkably, our analysis revealed high levels of coevolution of Tethya and their associated microbial communities, even when microbiomes were analysed at the genus level. Our findings support the use of an integrative approach to better understand the evolutionary history of sponges. http://zoobank.org/urn:lsid:zoobank.org:pub:54F80E5E-99A4-43CE-BA3A-BCD01E715595

Intratumoral and fecal microbiota reveals microbial markers associated with gastric carcinogenesis.

The relationship between dysbiosis of the gastrointestinal microbiota and gastric cancer (GC) has been extensively studied. However, microbiota alterations in GC patients vary widely across studies, and reproducible diagnostic biomarkers for early GC are still lacking in multiple populations. Thus, this study aimed to characterize the gastrointestinal microbial communities involved in gastric carcinogenesis through a meta-analysis of multiple published and open datasets. We analyzed 16S rRNA sequencing data from 1,642 gastric biopsy samples and 394 stool samples across 11 independent studies. VSEARCH, QIIME and R packages such as vegan, phyloseq, cooccur, and random forest were used for data processing and analysis. PICRUSt software was employed to predict functions. The α-diversity results indicated significant differences in the intratumoral microbiota of cancer patients compared to non-cancer patients, while no significant differences were observed in the fecal microbiota. Network analysis showed that the positive correlation with GC-enriched bacteria increased, and the positive correlation with GC-depleted bacteria decreased compared to healthy individuals. Functional analyses indicated that pathways related to carbohydrate metabolism were significantly enriched in GC, while biosynthesis of unsaturated fatty acids was diminished. Additionally, we investigated non-Helicobacter pylori (HP) commensals, which are crucial in both HP-negative and HP-positive GC. Random forest models, constructed using specific taxa associated with GC identified from the LEfSe analysis, revealed that the combination of Lactobacillus and Streptococcus included alone could effectively discriminate between GC patients and healthy individuals in fecal samples (area under the curve (AUC) = 0.7949). This finding was also validated in an independent cohort (AUC = 0.7712). This study examined the intratumoral and fecal microbiota of GC patients from a dual microecological perspective and identified Lactobacillus, Streptococcus, Roseburia, Faecalibacterium and Phascolarctobacterium as intratumoral and intestinal-specific co-differential bacteria. Furthermore, it confirmed the validity of the combination of Lactobacillus and Streptococcus as GC-specific microbial markers across multiple populations, which may aid in the early non-invasive diagnosis of GC.

16S rRNA phylogeny and clustering is not a reliable proxy for genome-based taxonomy in Streptomyces.

Streptomyces is among the most extensively studied genera of bacteria but its complex taxonomy remains contested and is suspected to contain significant species-level misclassification. Resolving the classification of Streptomyces would benefit many areas of applied microbiology that rely on an accurate ground truth for grouping of related organisms, including comparative genomics-based searches for novel antimicrobials. We survey taxonomic conflicts between 16S rRNA and whole genome-based Streptomyces classifications using 2276 publicly available Streptomyces genome assemblies and 48 981 publicly available full-length 16S rRNA Streptomyces sequences from silva, Greengenes, Ribosomal Database Project (RDP), and NCBI (National Centre for Biotechnology Information) databases. We construct a full-length 16S gene tree for 14 239 distinct Streptomyces sequences that resolves three major lineages of Streptomyces, but whose topology is not consistent with existing taxonomic assignments. We use these sequence data to delineate 16S and whole genome landscapes for Streptomyces, demonstrating that 16S and whole-genome classifications are frequently in disagreement, and that 16S zero-radius Operational Taxonomic Units (zOTUs) are often inconsistent with Average Nucleotide Identity (ANI)-based taxonomy. Our results strongly imply that 16S rRNA sequence data does not map to taxonomy sufficiently well to delineate Streptomyces species routinely. We propose that alternative marker sequences should be adopted by the community for classification and metabarcoding. Insofar as Streptomyces taxonomy has been determined or supported by 16S sequence data and may in parts be in error, we also propose that reclassification of the genus by alternative approaches may benefit the Streptomyces community.

Genetic Variability in Mediterranean Coastal Ecosystems: Insights into Ostrea spp. (Bivalvia: Ostreidae).

Oysters are sessile, filter-feeding bivalve molluscs widely distributed in estuarine and coastal habitats worldwide. They constitute a valuable resource for fisheries and extensive aquaculture and provide essential ecological services. Yet, their genetic diversity and distribution remain understudied. The variability in shell morphology complicates species classification, which is influenced by environmental and genetic factors. Although molecular phylogenetics research has refined oyster taxonomy and identified approximately 100 extant species, numerous taxonomic uncertainties persist. In the present study, we aimed to document the occurrence of small flat oysters of the genus Ostrea along the Mediterranean coastal areas of Liguria and Sardinia (Italy). Specifically, 16S rRNA sequence data were used to identify Ostrea species. Our findings offer novel insights into the O. stentina species complex and O. neostentina, a new species in the Mediterranean coastal areas of Italy. The study data further our understanding of Ostrea species diversity, distribution, and evolutionary patterns.

Gender-specific microbial signatures in saliva: Unveiling the association between the oral microbiome and the pathogenesis of glioma

The intricate interplay between the human oral microbiome and systemic health is increasingly being recognized, particularly in the context of central nervous system pathologies such as glioblastoma. In this study, we aimed to elucidate gender-specific differences in the salivary microbiome of glioma patients by utilizing 16S rRNA sequencing data from publicly available salivary microbiome datasets. We conducted comprehensive bioinformatics analysis, encompassing quality control, noise reduction, species classification, and microbial community composition analysis at various taxonomic levels. Machine learning algorithms were employed to identify microbial signatures associated with glioma. When compared to healthy controls, our analysis revealed distinct differences in the salivary microbiota of glioma patients. Notably, the genera Leptotrichia and Atopobium exhibited significant variations in abundance between genders. Leptotrichia was prevalent in healthy females but exhibited a reduced abundance in female glioma patients. In contrast, Atopobium was more abundant in male glioma patients. These findings suggest that hormonal influences might play a role in shaping the salivary microbiome and its association with glioma. We utilized a combination of LASSO-logistic regression and random forest models for feature selection, and identified key microbial features that differentiated glioma patients from healthy controls. We developed a diagnostic model with high predictive accuracy and area under the curve and principal component analysis metrics confirmed its robustness. The analysis of microbial markers, including Atopobium and Leptotrichia, highlighted the potential of the salivary microbiota as a non-invasive biomarker for the diagnosis and prognosis of glioma. Our findings highlight significant gender-specific disparities in the salivary microbiome of patients with glioma, offering new insights into the pathogenesis of glioma and paving the way for innovative diagnostic and therapeutic strategies. The use of saliva as a diagnostic fluid, given its ease of collection and non-invasive nature, holds immense promise for monitoring systemic health and the trajectory of disease. Future research should focus on investigating the underlying mechanisms by which the salivary microbiome influences the development of glioma and identifying potential microbiome-targeted therapies to enhance the management of glioma.

A cross-species analysis of fecal microbiomes in humans and mice reveals similarities and dissimilarities associated with prostate cancer risk.

Prostate cancer is a complex disease that develops over time and is influenced by several lifestyle factors that also impact gut microbes. Gut dysbiosis is intricately linked to prostate carcinogenesis, but the precise mechanisms remain poorly understood. Mice are crucial for studying the relationships between gut microbes and prostate cancer, but discovering similarities between humans and mice may aid in elucidating new mechanisms. We used 16s rRNA sequencing data from stool samples of tumor-bearing prostate-specific conditional Pten-knockout mice, disease-free wildtype mice, and a human cohort suspected of having prostate cancer to conduct taxonomic and metagenomic profiling. Features were associated with prostate cancer status and low risk (a negative biopsy of Gleason grade <2) or high risk (Gleason grade ≥2) in humans. In both humans and mice, community composition differed between individuals with and without prostate cancer. Odoribacter spp. and Desulfovibrio spp. were taxa associated with prostate cancer in mice and humans. Metabolic pathways associated with cofactor and vitamin synthesis were common in mouse and human prostate cancer, including bacterial synthesis of folate (vitamin B9), ubiquinone (CoQ10), phylloquinone (vitamin K1), menaquinone (vitamin K2), and tocopherol (vitamin E). Our study provides valuable data that can help bridge the gap between human and mouse microbiomes. Our findings provide evidence to support the notion that certain bacterial-derived metabolites may promote prostate cancer, as well as a preclinical model that can be used to characterize biological mechanisms and develop preventive interventions.

Evaluating urine volume and host depletion methods to enable genome-resolved metagenomics of the urobiome.

The gut microbiome has emerged as a clear player in health and disease, in part by mediating host response to environment and lifestyle. The urobiome (microbiota of the urinary tract) likely functions similarly. However, efforts to characterize the urobiome and assess its functional potential have been limited due to technical challenges including low microbial biomass and high host cell shedding in urine. Here, to begin addressing these challenges, we evaluate urine sample volume (100 ml - 5 mL), and host DNA depletion methods and their effects on urobiome profiles in healthy dogs, which are a robust large animal model for the human urobiome. We collected urine from seven dogs and fractionated samples into aliquots. One set of samples was spiked with host (canine) cells to model a biologically relevant host cell burden in urine. Samples then underwent DNA extraction followed by 16S rRNA gene and shotgun metagenomic sequencing. We then assembled metagenome assembled genomes (MAGs) and compared microbial composition and diversity across groups. We tested six methods of DNA extraction: QIAamp BiOstic Bacteremia (no host depletion), QIAamp DNA Microbiome, Molzym MolYsis, NEBNext Microbiome DNA Enrichment, Zymo HostZERO, and Propidium Monoazide. In relation to urine sample volume, 3 3.0 mL resulted in the most consistent urobiome profiling. In relation to host depletion, individual (dog) but not extraction method drove overall differences in microbial composition. DNA Microbiome yielded the greatest microbial diversity in 16S rRNA sequencing data and shotgun metagenomic sequencing data, and maximized MAG recovery while effectively depleting host DNA in host-spiked urine samples. As proof-of-principle, we then mined MAGs for core metabolic functions and environmental chemical metabolism. We identified long chain alkane utilization in two of the urine MAGs. Long chain alkanes are common pollutants that result from industrial combustion processes and end up in urine. This is the first study, to our knowledge, to demonstrate environmental chemical degradation potential in urine microbes through genome-resolved metagenomics. These findings provide guidelines for studying the urobiome in relation to sample volume and host depletion, and lay the foundation for future evaluation of urobiome function in relation to health and disease.

Abstract Tu022: Gut-derived bacterial metabolites promote cardiac inflammation, leading to heart failure in murine model of DSS-induced colitis

Background: Inflammatory Bowel Disease (IBD) affects millions of people all over the world. This has been known to contribute to several other comorbidities. Recently, impaired gut-barrier permeability has been known to contribute to cardiovascular diseases in IBD patients. However, the precise mechanisms remain unclear. In this study, we propose that bacterial metabolites, like peptidoglycan, may play a role in promoting cardiac inflammation and ultimately leading to heart failure in mice following colitis. Methods: We induced colitis in C57 black mice with DSS (dextran sodium sulfate) treatment. Further, we collected fecal sample for 16s rRNA analysis to evaluate microbial flora. Additionally, heart and intestine tissues were collected to determine inflammatory genes expression (qPCR), immune cell infiltration (FACS), and intestinal permeability (WB and immunohistochemistry). Blood was collected to quantify the circulating peptidoglycan (PGN) level. The effect of PGN on NFkB inflammatory pathways was evaluated in macrophages. Results: Echo-data showed cardiac dysfunction in DSS mice. Masson’s trichrome staining showed deteriorated intestinal villi, while western blot data indicated decreased Occludin and increased PV-1 level, suggesting a leaky gut in DSS treated mice. Comparative analysis of 16s rRNA sequencing data showed significantly altered Firmicutes/Bacteroidetes ratio in DSS treated mice as compared to control mice. Moreover, plasma PGN level was significantly increased 3 weeks following DSS treatment. Real-time gene expression data of pro-inflammatory cytokines (TNFα) and fibrosis associated genes (col1α1, fibronectin) were increased in DSS mice heart. Increased CD68 expression in the immunostaining data of DSS mice heart suggests the recruitment of inflammatory macrophages. At the molecular level, CDK9 expression was significantly increased in macrophage following PGN treatment, which ultimately leads to an increase in NFkB signaling. Finally, CDK9 inhibition attenuated PGN-induced NFkB signaling. Conclusion: Together, our study suggests that colitis-induced gut leakage leads to CDK9-mediated NFκB upregulation, contributing to cardiac inflammation. Ongoing research will shed light on the underlying molecular mechanisms of cardiovascular dysfunction.

Soil pH and Nutrient Content Sustain Variability of Soil Bacterial Community Structure and Activity after Forest Clear-Cutting

Clear-cutting is the most robust intervention in a forest ecosystem, causing marked changes in ecosystem processes. Although the effects of forest harvesting have been widely investigated, comparative studies can provide vital supplementary information concerning specific fields, including changes in soil microbiota structure and functioning. Our study examined the soil bacterial community composition, diversity, and activity of a mixed pedunculate oak stand over three years after clear-cutting based on 16S rRNA sequencing and substrate-induced respiration data. In addition, we conducted a yearly comparison with a control oak stand already in the regeneration phase. According to our results, the forest harvest caused only limited changes in the diversity, structure, and activity of the soil bacterial community of the oak stand, suggesting that soil parameters influence the soil bacterial community structure and functioning more significantly than the cessation of forest cover.

Adolescent gut microbiome imbalance and its association with immune response in inflammatory bowel diseases and obesity

BackgroundRecently, there has been an increase in the number of studies focusing on the association between the gut microbiome and obesity or inflammatory diseases, especially in adults. However, there is a lack of studies investigating the association between gut microbiome and gastrointestinal (GI) diseases in adolescents.MethodWe obtained 16S rRNA-seq datasets for gut microbiome analysis from 202 adolescents, comprising ulcerative colitis (UC), Crohn’s disease (CD), obesity (Ob), and healthy controls (HC). We utilized Quantitative Insights Into Microbial Ecology (QIIME) and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) to acquire Operational Taxonomic Units (OTUs). Subsequently, we analyzed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) terms and pathway enrichment for the identified OTUs.ResultsIn this study, we investigated the difference between the gut microbiomes in adolescents with GI diseases and those in healthy adolescents using 202 samples of 16S rRNA sequencing data. The distribution of the six main gut microbiota (i.e., unclassified Dorea, unclassified Lachnospiraceae, unclassified Ruminococcus, Faecalibacterium prausnitzii, Prevotella copri, unclassified Sutterella) was different based on the status of obesity and inflammatory diseases. Dysbiosis was observed within Lachnospiraceae in adolescents with inflammatory diseases (i.e., UC and CD), and in adolescents with obesity within Prevotella and Sutterella. More specifically, our results showed that the relative abundance of Faecalibacterium prausnitzii and unclassified Lachnospiraceae was more than 10% and 8% higher, respectively, in the UC group compared to the CD, Ob, and HC groups. Additionally, the Ob group had over 20% and over 3% higher levels of Prevotella copri and unclassified Sutterella, respectively, compared to the UC, CD, and HC groups. Also, inspecting associations between the six specific microbiota and KO terms, we found that the six microbiota -relating KO terms were associated with NOD-like receptor signaling. These six taxa differences may affect the immune system and inflammatory response by affecting NOD-like receptor signaling in the host during critical adolescence.ConclusionIn this study, we discovered that dysbiosis of the microbial community had varying degrees of influence on the inflammatory and immune response pathways in adolescents with inflammatory diseases and obesity.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725

Familial clustering of dysbiotic oral and fecal microbiomes in juvenile dermatomyositis

Juvenile dermatomyositis (JDM) is a rare immune-mediated disease of childhood with putative links to microbial exposures. In this multi-center, prospective, observational cohort study, we evaluated whether JDM is associated with discrete oral and gut microbiome signatures. We generated 16S rRNA sequencing data from fecal, saliva, supragingival, and subgingival plaque samples from JDM probands (n = 28). To control for genetic and environmental determinants of microbiome community structure, we also profiled microbiomes of unaffected family members (n = 27 siblings, n = 26 mothers, and n = 17 fathers). Sample type (oral-vs-fecal) and nuclear family unit were the predominant variables explaining variance in microbiome diversity, more so than having a diagnosis of JDM. The oral and gut microbiomes of JDM probands were more similar to their own unaffected siblings than they were to the microbiomes of other JDM probands. In a sibling-paired within-family analysis, several potentially immunomodulatory bacterial taxa were differentially abundant in the microbiomes of JDM probands compared to their unaffected siblings, including Faecalibacterium (gut) and Streptococcus (oral cavity). While microbiome features of JDM are often shared by unaffected family members, the loss or gain of specific fecal and oral bacteria may play a role in disease pathogenesis or be secondary to immune dysfunction in susceptible individuals.

Gut microbiota dysbiosis in hyperuricaemia promotes renal injury through the activation of NLRP3 inflammasome

BackgroundThe prevalence of hyperuricaemia (HUA), a metabolic disorder characterized by elevated levels of uric acid, is on the rise and is frequently associated with renal injury. Gut microbiota and gut-derived uremic toxins are critical mediators in the gut-kidney axis that can cause damage to kidney function. Gut dysbiosis has been implicated in various kidney diseases. However, the role and underlying mechanism of the gut microbiota in HUA-induced renal injury remain unknown.ResultsA HUA rat model was first established by knocking out the uricase (UOX). HUA rats exhibited apparent renal dysfunction, renal tubular injury, fibrosis, NLRP3 inflammasome activation, and impaired intestinal barrier functions. Analysis of 16S rRNA sequencing and functional prediction data revealed an abnormal gut microbiota profile and activation of pathways associated with uremic toxin production. A metabolomic analysis showed evident accumulation of gut-derived uremic toxins in the kidneys of HUA rats. Furthermore, faecal microbiota transplantation (FMT) was performed to confirm the effects of HUA-induced gut dysbiosis on renal injury. Mice recolonized with HUA microbiota exhibited severe renal injury and impaired intestinal barrier functions following renal ischemia/reperfusion (I/R) surgery. Notably, in NLRP3-knockout (NLRP3−/−) I/R mice, the deleterious effects of the HUA microbiota on renal injury and the intestinal barrier were eliminated.ConclusionOur results demonstrate that HUA-induced gut dysbiosis contributes to the development of renal injury, possibly by promoting the production of gut-derived uremic toxins and subsequently activating the NLRP3 inflammasome. Our data suggest a potential therapeutic strategy for the treatment of renal diseases by targeting the gut microbiota and the NLRP3 inflammasome.-txnFZYkVzJbh2g8RkPjrkVideo Graphical

Role of gut microbiota in the pathogenesis of castration-resistant prostate cancer: a comprehensive study using sequencing and animal models.

CRPC remains a significant challenge in prostate cancer research. We aimed to elucidate the role of gut microbiota and its specific mechanisms in CRPC using a multidisciplinary approach. We analyzed 16S rRNA sequencing data from mouse fecal samples, revealing substantial differences in gut microbiota composition between CRPC and castration-sensitive prostate cancer mice, particularly in Firmicutes and Bacteroidetes. Functional analysis suggested different bacteria may influence CRPC via the α-linolenic acid metabolism pathway. In vivo, experiments utilizing mouse models and fecal microbiota transplantation (FMT) demonstrated that FMT from healthy control mice could decelerate tumor growth in CRPC mice, reduce TNF-α levels, and inhibit the activation of the TLR4/MyD88/NF-κB signaling pathway. Transcriptome sequencing identified crucial genes and pathways, with rescue experiments confirming the gut microbiota's role in modulating CRPC progression through the TLR4/MyD88/NF-κB pathway. The activation of this pathway by TNF-α has been corroborated by in vitro cell experiments, indicating its role in promoting prostate cancer cell proliferation, migration, and invasion while inhibiting apoptosis. Gut microbiota dysbiosis may promote CRPC development through TNF-α activation of the TLR4/MyD88/NF-κB signaling pathway, potentially linked to α-linolenic acid metabolism.

WeiNaiAn capsule attenuates intestinal mucosal injury and regulates gut microbiome in indomethacin-induced rat

Indomethacin, as a non-steroidal anti-inflammatory drugs, is widely used in the clinic. However, it can cause severe injury to the gastrointestinal tract and the incidence is increasing. It has become an essential clinical problem in preventing intestinal damage. Teprenone has been reported to have a significant positive effect on intestinal mucosal lesions, but long-term use of teprenone can elicit adverse reactions. WeiNaiAn capsule is a traditional Chinese medicine formulation used widely in the treatment of gastric and duodenal mucosal injury. However, how WeiNaiAn protects against intestinal mucosal injury and its mechanism of action are not known. In this study, WeiNaiAn capsule or Teprenone treatment improved the intestinal mucosal pathological score and antioxidant level in indomethacin-induced rats. 16 S rRNA sequence data showed WeiNaiAn capsule reverted the structure community and replenished the beneficial bacteria. Furthermore, fingerprint analysis revealed multiple components of WeiNaiAn capsule, including calycosin glucoside, ginsenoside Rg1, ginsenoside Rb1, taurocholic acid sodium, formonetin, and calycosin glucoside. The components of WeiNaiAn capsule promoted the wound healing of the epithelial cell in vitro. Moreover, the components of WeiNaiAn capsule inhibited the protein expressions of phosphoinositide 3-kinase /protein kinase B /mammalian target of rapamycin in hydrogen peroxide or lipopolysaccharides-induced cell model. In conclusion, WeiNaiAn capsule improves intestinal mucosal injury by regulating cell migration, enhancing antioxidant activity, and promoting the structure of the bacterial community homeostasis, the multiple targets provide the parameters for the treatment in the clinic.